EconPapers    
Economics at your fingertips  
 

Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes

D.W. van der Meer, M. Shepero, A. Svensson, J. Widén and J. Munkhammar

Applied Energy, 2018, vol. 213, issue C, 195-207

Abstract: This paper presents a study into the utilization of Gaussian Processes (GPs) for probabilistic forecasting of residential electricity consumption, photovoltaic (PV) power generation and net demand of a single household. The covariance function that encodes prior belief on the general shape of the time series plays a vital role in the performance of GPs and a common choice is the squared exponential (SE), although it has been argued that the SE is likely suboptimal for physical processes. Therefore, we thoroughly test various (combinations of) covariance functions. Furthermore, in order bypass the substantial learning and inference time accompanied with GPs, we investigate the potential of dynamically updating the hyperparameters using a moving training window and assess the consequences on predictive accuracy. We show that the dynamic GP produces sharper prediction intervals (PIs) than the static GP with significant lower computational burden, but at the cost of the ability to capture sharp peaks. In addition, we examine the difference in accuracy between a direct and indirect forecasting strategy in case of net demand forecasting and show that the latter is prone to producing wider PIs with higher coverage probability.

Keywords: Gaussian Processes; PV; Residential electricity consumption; Net demand; Probabilistic; Forecasting (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261917318275
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:213:y:2018:i:c:p:195-207

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

 
Page updated 2018-07-21
Handle: RePEc:eee:appene:v:213:y:2018:i:c:p:195-207